IET Electrical Systems in Transportation is aimed at all aspects of electrical power systems in modern transport applications including generation, storage, distribution and utilisation. The scope extends to all sectors of transportation: aerospace, marine (including sub-sea), automotive or land-based and rail. The central theme of the journal is to focus on the system and sub-system aspects of electrical energy including system architectures and integration, energy management, control and protection.

This study proposes a novel hybrid energy storage system (HESS) composed of a battery pack and a superconducting magnetic energy storage (SMES) for electric vehicle. Typically, the SMES has a higher power density and lower energy density than other energy storage devices, while battery has higher energy density. Thus, the overall HESS performance can be increased in power and energy density by combining SMES and batteries. The proposed HESS is designed based on bidirectional Z-source inverter (ZSI). Compared to other SMES/battery-based HESS topologies that are two stage designs (including DC/DC and AC/DC converters), in this topology, SMES and battery can be incorporated into the Z-source network which results in lower cost and improved HESS performance. Furthermore, the battery converter has been eliminated due to the buck/boost feature of the ZSI. The fuzzy control method and filters are used to distribute power between the SMES and battery. This study also describes the proposed HESS performance principles and its operation in different modes.

Electric vehicle (EV) integration into the power grids is increasing rapidly. To analyse the effect of charging of EVs on the distribution system, most of the literature considered EV load as constant power load (CPL) which do not represent the exact behaviour of these uncertain loads. An accurate EV load modelling is developed by determining the relationship between power consumption by EV, grid voltage and state of charges of fast charging EV load. The derived relationship is validated by simulating a realistic fast charging system to obtain a battery charging behaviour characteristics and is curve fitted on standard exponential load model. Further the impact of stochastic 24-h load profile of fast charging EVs considering the exponential load model is investigated on IEEE 123 bus distribution system and is compared with the constant impedance-constant current-constant power (ZIP) load model and CPL model. The stochastic 24-h load is developed using queuing analysis-based method. The results show that the exponential load model is the better representation of fast charging EV load and 10.19% of the reduction in annual energy demand and 11.19% of the reduction in annual energy loss is observed for exponential load model compared to the existing CPL model.

Wide-bandgap (WBG) devices such as SiC and GaN switches are regarded as next-generation power semiconductors, due to their superior performance over conventional Si devices, for instance, a low switching loss and high thermal conductivity. Its bottleneck, however, is the high cost, which is critical for renewable energy and automotive industries. This study adopts SWISS AC/DC rectifier topology for the three-phase 380–480 VAC along with an isolated DC/DC converter, indicating such topology can maximise the advantages of Si (low conduction loss) and SiC (high switching loss), altogether thereby yielding the high performance and low cost. A novel space-vector pulse width modulation (SVPWM) was proposed to control such a current-source power factor correction, where only two SiC devices were adopted for the DC-bus voltage control. The closed-loop control of the grid current is realised for the unity power factor. Such topology further allows the DC-bus voltage to be varied with the output voltage, thereby minimising the system loss. A final prototype was built to charge a 48 V battery at 11 kW. Experimental results validated the effectiveness of such battery charger design.

The battery management system in electrified transportation requires an accurate battery model for online state estimation of the battery. The parameters of the battery model depend upon state of charge, C-rate, and temperature. A detailed battery model defined by 31 polynomial coefficients is used for determination of battery parameters. The parameter estimation is formulated as an optimisation problem and six different meta-heuristic optimisation techniques are utilised for solving it. The efficiency of optimisation techniques is compared in terms of solution quality, computation efficiency, and convergence characteristics. Further, their performance is analysed statistically using parametric (t-test) and non-parametric tests (Wilcoxon test). The parameters values estimated by applying optimisation techniques are cross-validated with value of parameters extracted using standard constant-current pulse charge–discharge test to establish the effectiveness of the proposed approach.